Things are working in principle but you lack visibility? How much money could you save?
How can you optimize unit economics?

Interfaces between teams or systems usually cause friction. Let’s tweak your data strategy and systems. Now is always a good time!

Work Samples

Proof of Concept

Task:

The idea was to see how app detected mobility data provides valuable insights into the whereabouts of people throughout the day and identifies hot spots of activity for store planning. Furthermore, it was of interest to see if places of origin can be identified for specified arrival zones where the stores are located.

Approach:

We extracted raw data of arrival and departure events (around 4.5 mio) that were detected on phones in the background. After cleaning that data for our purposes, we transformed it to extract additional information about travels and stays by connecting different data points. Finally, we build several visualizations to make that data accessible.

App Piloting

Task:

To evaluate the performance of tracking truck arrival and departure for the purpose of a more efficient use and allocation of parking spaces, an approach needed to be developed that allowed to measure the accuracy of the detection given the special circumstances regarding devices, mode of transportation and driving patters.

Approach:

We developed a test app with mobility detection following the visual guidelines of the client to evaluate performance by comparing the automated detections with manually logged events. Drivers had to give their feedback in different situations with minimal distraction (one click) on their tablet or smartphone devices that are installed in the trucks.

Machine Learning

Task:

For many use cases it is critical or at least helpful to understand in real-time what mode of transport like car, bicycle, or train a user is using. That information should be available without any user input, but rather by the pure analysis of sensor data on a mobile device in the background with minimal battery drain.

Approach:

To begin, we collected thousands of trips where users labeled the data, telling us what mode of transport they were using. For these trips we recorded sensor data for different smartphone sensors like gyroscope or accelerometer. Based on that data we used machine learning to identify patterns and recognize the different modes.

User Research

Task:

To solve the parking problem, the European Commission has invested into a consumer app that guides people to available on-street parking spots. To avoid driver distraction, the system needs to be very simple and intuitive, yet still provide all the necessary information to find a nearby parking spot.

Approach:

In the different countries targeted for the rollout of the solution, we interviewed car drivers about their parking search behavior, tips of finding a spot, and use of mobile devices. We tested different concepts and prototypes with them to determine the best approach how to wrap the functionality into a lean and simple user interface.